| /* |
| * Copyright (c) 2017 ARM Limited. |
| * |
| * SPDX-License-Identifier: MIT |
| * |
| * Permission is hereby granted, free of charge, to any person obtaining a copy |
| * of this software and associated documentation files (the "Software"), to |
| * deal in the Software without restriction, including without limitation the |
| * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or |
| * sell copies of the Software, and to permit persons to whom the Software is |
| * furnished to do so, subject to the following conditions: |
| * |
| * The above copyright notice and this permission notice shall be included in all |
| * copies or substantial portions of the Software. |
| * |
| * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR |
| * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, |
| * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE |
| * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER |
| * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, |
| * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE |
| * SOFTWARE. |
| */ |
| #include "arm_compute/core/NEON/kernels/NEDirectConvolutionLayerOutputStageKernel.h" |
| |
| #include "arm_compute/core/AccessWindowStatic.h" |
| #include "arm_compute/core/Error.h" |
| #include "arm_compute/core/Helpers.h" |
| #include "arm_compute/core/ITensor.h" |
| #include "arm_compute/core/NEON/NEFixedPoint.h" |
| #include "arm_compute/core/Types.h" |
| #include "arm_compute/core/Validate.h" |
| #include "arm_compute/core/Window.h" |
| |
| #include <arm_neon.h> |
| #include <cstddef> |
| #include <cstdint> |
| |
| using namespace arm_compute; |
| |
| namespace |
| { |
| Status validate_arguments(const ITensorInfo *input, const ITensorInfo *bias, const ITensorInfo *output) |
| { |
| ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(input); |
| ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::QS8, DataType::QS16, DataType::F16, DataType::QS32, DataType::F32); |
| |
| if(bias != nullptr) |
| { |
| ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(bias, 1, DataType::QS8, DataType::QS16, DataType::F16, DataType::QS32, DataType::F32); |
| |
| if(is_data_type_quantized(input->data_type())) |
| { |
| ARM_COMPUTE_RETURN_ERROR_ON_MSG(input->data_type() == DataType::QS8 && bias->data_type() != DataType::QS8, "Wrong data type for bias"); |
| ARM_COMPUTE_RETURN_ERROR_ON_MSG(input->data_type() == DataType::QS16 && bias->data_type() != DataType::QS8, "Wrong data type for bias"); |
| ARM_COMPUTE_RETURN_ERROR_ON_MSG(input->data_type() == DataType::QS32 && bias->data_type() != DataType::QS16, "Wrong data type for bias"); |
| } |
| else |
| { |
| ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input, bias); |
| } |
| |
| ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_FIXED_POINT_POSITION(input, bias); |
| ARM_COMPUTE_RETURN_ERROR_ON(bias->num_dimensions() > 1); |
| } |
| else |
| { |
| ARM_COMPUTE_RETURN_ERROR_ON_MSG(!is_data_type_quantized(input->data_type()), "Calling output stage kernel with floating point arguments"); |
| } |
| |
| // Checks performed when output is configured |
| if((output != nullptr) && (output->total_size() != 0)) |
| { |
| ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(output, 1, DataType::QS8, DataType::QS16, DataType::F32); |
| if(is_data_type_quantized(input->data_type())) |
| { |
| ARM_COMPUTE_RETURN_ERROR_ON_MSG(input->data_type() == DataType::QS8 && output->data_type() != DataType::QS8, "Wrong data type for output"); |
| ARM_COMPUTE_RETURN_ERROR_ON_MSG(input->data_type() == DataType::QS16 && output->data_type() != DataType::QS8, "Wrong data type for output"); |
| ARM_COMPUTE_RETURN_ERROR_ON_MSG(input->data_type() == DataType::QS32 && output->data_type() != DataType::QS16, "Wrong data type for output"); |
| } |
| else |
| { |
| ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input, output); |
| } |
| ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_FIXED_POINT_POSITION(input, output); |
| } |
| |
| return Status{}; |
| } |
| |
| std::pair<Status, Window> validate_and_configure_window(ITensorInfo *input, ITensorInfo *bias, ITensorInfo *output) |
| { |
| bool window_changed = false; |
| const unsigned int num_elems_processed_per_iteration = 16 / element_size_from_data_type(input->data_type()); |
| |
| // Configure kernel window |
| Window win = calculate_max_window(*input, Steps(num_elems_processed_per_iteration)); |
| AccessWindowHorizontal input_access(input, 0, num_elems_processed_per_iteration); |
| |
| if(output != nullptr && (output->total_size() != 0)) |
| { |
| AccessWindowHorizontal output_access(output, 0, num_elems_processed_per_iteration); |
| |
| if(bias == nullptr) |
| { |
| window_changed = update_window_and_padding(win, input_access, output_access); |
| } |
| else |
| { |
| AccessWindowStatic bias_access(bias, 0, 0, bias->dimension(0), bias->dimension(1)); |
| window_changed = update_window_and_padding(win, input_access, output_access, bias_access); |
| } |
| |
| output_access.set_valid_region(win, ValidRegion(Coordinates(), output->tensor_shape())); |
| } |
| else |
| { |
| if(bias == nullptr) |
| { |
| window_changed = update_window_and_padding(win, input_access); |
| } |
| else |
| { |
| AccessWindowStatic bias_access(bias, 0, 0, bias->dimension(0), bias->dimension(1)); |
| window_changed = update_window_and_padding(win, input_access, bias_access); |
| } |
| |
| input_access.set_valid_region(win, ValidRegion(Coordinates(), input->tensor_shape())); |
| } |
| |
| Status err = (window_changed) ? ARM_COMPUTE_CREATE_ERROR(ErrorCode::RUNTIME_ERROR, "Insufficient Padding!") : Status{}; |
| return std::make_pair(err, win); |
| } |
| |
| // Internal load |
| inline float32x4_t internal_vld1q(const float *in) |
| { |
| return vld1q_f32(in); |
| } |
| inline qint8x16_t internal_vld1q(const qint8_t *in) |
| { |
| return vld1q_qs8(in); |
| } |
| inline qint16x8_t internal_vld1q(const qint16_t *in) |
| { |
| return vld1q_qs16(in); |
| } |
| |
| inline qint32x4_t internal_vld1q(const qint32_t *in) |
| { |
| return vld1q_s32(in); |
| } |
| |
| // Internal store |
| inline void internal_vst1q(float *p, const float32x4_t &v) |
| { |
| vst1q_f32(p, v); |
| } |
| inline void internal_vst1q(qint8_t *p, const qint8x16_t &v) |
| { |
| vst1q_qs8(p, v); |
| } |
| inline void internal_vst1q(qint8_t *p, const qint16x8_t &v) |
| { |
| vst1_qs8(p, vqmovn_s16(v)); |
| } |
| inline void internal_vst1q(qint16_t *p, const qint16x8_t &v) |
| { |
| vst1q_qs16(p, v); |
| } |
| |
| inline void internal_vst1q(qint32_t *p, const qint32x4_t &v) |
| { |
| vst1q_s32(p, v); |
| } |
| |
| inline void internal_vst1q(qint16_t *p, const qint32x4_t &v) |
| { |
| vst1_qs16(p, vqmovn_qs32(v)); |
| } |
| |
| // Internal vdup |
| inline float32x4_t internal_vdupq_n(float v) |
| { |
| return vdupq_n_f32(v); |
| } |
| inline qint8x16_t internal_vdupq_n(qint8_t v) |
| { |
| return vdupq_n_qs8(v); |
| } |
| inline qint16x8_t internal_vdupq_n(qint16_t v) |
| { |
| return vdupq_n_qs16(v); |
| } |
| |
| inline qint32x4_t internal_vdupq_n(qint32_t v) |
| { |
| return vdupq_n_qs32(v); |
| } |
| |
| // Internal vadd |
| inline float32x4_t internal_vqaddq(const float32x4_t &x, const float32x4_t &y) |
| { |
| return vaddq_f32(x, y); |
| } |
| inline qint8x16_t internal_vqaddq(const qint8x16_t &x, const qint8x16_t &y) |
| { |
| return vqaddq_qs8(x, y); |
| } |
| inline qint16x8_t internal_vqaddq(const qint16x8_t &x, const qint16x8_t &y) |
| { |
| return vqaddq_qs16(x, y); |
| } |
| inline qint32x4_t internal_vqaddq(const qint32x4_t &x, const qint32x4_t &y) |
| { |
| return vqaddq_qs32(x, y); |
| } |
| |
| #ifdef __ARM_FEATURE_FP16_VECTOR_ARITHMETIC |
| inline float16x8_t internal_vld1q(const float16_t *in) |
| { |
| return vld1q_f16(in); |
| } |
| inline void internal_vst1q(float16_t *p, const float16x8_t &v) |
| { |
| vst1q_f16(p, v); |
| } |
| inline float16x8_t internal_vdupq_n(float16_t v) |
| { |
| return vdupq_n_f16(v); |
| } |
| inline float16x8_t internal_vqaddq(const float16x8_t &x, const float16x8_t &y) |
| { |
| return vaddq_f16(x, y); |
| } |
| #endif /* __ARM_FEATURE_FP16_VECTOR_ARITHMETIC */ |
| |
| template <typename T1, typename T2, bool in_place, bool has_bias> |
| void output_stage(ITensor *input, const ITensor *bias, const Window window, ITensor *output) |
| { |
| Iterator in(input, window); |
| |
| if(in_place) // In place accumulate |
| { |
| execute_window_loop(window, [&](const Coordinates & id) |
| { |
| // Get bias and pointer to input |
| const auto in_ptr = reinterpret_cast<T1 *>(in.ptr()); |
| |
| // Accumulate bias |
| if(has_bias) |
| { |
| const auto vb = internal_vdupq_n(static_cast<T1>(*reinterpret_cast<const T2 *>(bias->ptr_to_element(Coordinates(id.z()))))); |
| internal_vst1q(in_ptr, internal_vqaddq(internal_vld1q(in_ptr), vb)); |
| } |
| else |
| { |
| internal_vst1q(in_ptr, internal_vld1q(in_ptr)); |
| } |
| }, |
| in); |
| } |
| else // Out of place accumulate |
| { |
| Iterator out(output, window); |
| execute_window_loop(window, [&](const Coordinates & id) |
| { |
| // Get bias and pointer to input |
| const auto in_ptr = reinterpret_cast<const T1 *>(in.ptr()); |
| const auto out_ptr = reinterpret_cast<T2 *>(out.ptr()); |
| |
| // Accumulate bias |
| if(has_bias) |
| { |
| const auto vb = internal_vdupq_n(static_cast<T1>(*reinterpret_cast<const T2 *>(bias->ptr_to_element(Coordinates(id.z()))))); |
| internal_vst1q(out_ptr, internal_vqaddq(internal_vld1q(in_ptr), vb)); |
| } |
| else |
| { |
| internal_vst1q(out_ptr, internal_vld1q(in_ptr)); |
| } |
| }, |
| in, out); |
| } |
| } |
| } // namespace |
| |
| NEDirectConvolutionLayerOutputStageKernel::NEDirectConvolutionLayerOutputStageKernel() |
| : _func(nullptr), _input(nullptr), _bias(nullptr), _output(nullptr) |
| { |
| } |
| |
| void NEDirectConvolutionLayerOutputStageKernel::configure(ITensor *input, const ITensor *bias, ITensor *output) |
| { |
| ARM_COMPUTE_ERROR_ON_NULLPTR(input); |
| |
| // Auto-initialize output output if required |
| if(output != nullptr) |
| { |
| // Output tensor auto initialization if not yet initialized |
| auto_init_if_empty(*output->info(), *input->info()); |
| } |
| |
| // Perform validation step |
| ARM_COMPUTE_ERROR_THROW_ON(validate_arguments(input->info(), (bias == nullptr) ? nullptr : bias->info(), (output == nullptr) ? nullptr : output->info())); |
| |
| _func = nullptr; |
| _bias = bias; |
| _input = input; |
| _output = output; |
| |
| // Configure kernel window |
| auto win_config = validate_and_configure_window(input->info(), (bias == nullptr) ? nullptr : bias->info(), (output == nullptr) ? nullptr : output->info()); |
| ARM_COMPUTE_ERROR_THROW_ON(win_config.first); |
| INEKernel::configure(win_config.second); |
| |
| // Set appropriate function |
| switch(input->info()->data_type()) |
| { |
| case DataType::QS8: |
| { |
| if(bias == nullptr) |
| { |
| _func = (output == nullptr) ? &output_stage<qint8_t, qint8_t, true, false> : &output_stage<qint8_t, qint8_t, false, false>; |
| } |
| else |
| { |
| _func = (output == nullptr) ? &output_stage<qint8_t, qint8_t, true, true> : &output_stage<qint8_t, qint8_t, false, true>; |
| } |
| break; |
| } |
| case DataType::QS16: |
| { |
| if(bias != nullptr && bias->info()->data_type() == DataType::QS8) |
| { |
| _func = (output == nullptr) ? &output_stage<qint16_t, qint8_t, true, true> : &output_stage<qint16_t, qint8_t, false, true>; |
| } |
| else if(bias == nullptr) |
| { |
| _func = (output == nullptr) ? &output_stage<qint16_t, qint8_t, true, false> : &output_stage<qint16_t, qint8_t, false, false>; |
| } |
| else |
| { |
| ARM_COMPUTE_ERROR("Not implemented"); |
| } |
| break; |
| } |
| case DataType::QS32: |
| { |
| _func = (output == nullptr) ? &output_stage<qint32_t, qint16_t, true, true> : &output_stage<qint32_t, qint16_t, false, true>; |
| break; |
| } |
| #ifdef __ARM_FEATURE_FP16_VECTOR_ARITHMETIC |
| case DataType::F16: |
| { |
| _func = (output == nullptr) ? &output_stage<float16_t, float16_t, true, true> : &output_stage<float16_t, float16_t, false, true>; |
| break; |
| } |
| #endif /* __ARM_FEATURE_FP16_VECTOR_ARITHMETIC */ |
| case DataType::F32: |
| { |
| _func = (output == nullptr) ? &output_stage<float, float, true, true> : &output_stage<float, float, false, true>; |
| break; |
| } |
| default: |
| { |
| ARM_COMPUTE_ERROR("Unsupported combination of types among the inputs."); |
| break; |
| } |
| } |
| } |
| |
| Status NEDirectConvolutionLayerOutputStageKernel::validate(const ITensorInfo *input, const ITensorInfo *bias, const ITensorInfo *output) |
| { |
| ARM_COMPUTE_RETURN_ON_ERROR(validate_arguments(input, bias, output)); |
| ARM_COMPUTE_RETURN_ON_ERROR(validate_and_configure_window(input->clone().get(), bias->clone().get(), output == nullptr ? nullptr : output->clone().get()).first); |
| |
| return Status{}; |
| } |
| |
| void NEDirectConvolutionLayerOutputStageKernel::run(const Window &window, const ThreadInfo &info) |
| { |
| ARM_COMPUTE_UNUSED(info); |
| ARM_COMPUTE_ERROR_ON_UNCONFIGURED_KERNEL(this); |
| ARM_COMPUTE_ERROR_ON_INVALID_SUBWINDOW(INEKernel::window(), window); |
| ARM_COMPUTE_ERROR_ON(_func == nullptr); |
| |
| (*_func)(_input, _bias, window, _output); |
| } |